Title :
The Clustered Causal State Algorithm: Efficient Pattern Discovery for Lossy Data-Compression Applications
Author :
Schmiedekamp, Mendel ; Subbu, Aparna ; Phoha, Shashi
Author_Institution :
Appl. Res. Lab., Penn State Univ.
Abstract :
Pattern discovery is a potential boon for data compression, but current approaches are inefficient and produce cumbersome pattern descriptions. The clustered causal state algorithm is a new pattern-discovery algorithm that incorporates recent clustering technology
Keywords :
data compression; data mining; pattern clustering; clustered causal state algorithm; data compression; pattern discovery; Bandwidth; Clustering algorithms; Communications technology; Costs; Data compression; Data mining; Entropy; History; Laboratories; Sensor systems and applications; clustering; model-based coding; pattern analysis; real-time systems; statistical pattern models;
Journal_Title :
Computing in Science & Engineering
DOI :
10.1109/MCSE.2006.98
